Smart Medical Systems Research Articles

Heart Sound Abnormality Detection From Multi-Institutional Collaboration: Introducing a Federated Learning Framework.

Early diagnosis of cardiovascular diseases is a crucial task in medical practice. With the application of computer audition in the healthcare field, artificial intelligence (AI) has been applied to clinical non-invasive intelligent auscultation of heart sounds to provide rapid and effective pre-screening. However, AI models generally require large amounts of data which may cause privacy issues. Unfortunately, it is difficult to collect large amounts of healthcare data from a single centre. In this study, we propose federated learning (FL) optimisation strategies for the practical application in multi-centre institutional heart sound databases. The horizontal FL is mainly employed to tackle the privacy problem by aligning the feature spaces of FL participating institutions without information leakage. In addition, techniques based on deep learning have poor interpretability due to their "black-box" property, which limits the feasibility of AI in real medical data. To this end, vertical FL is utilised to address the issues of model interpretability and data scarcity. Experimental results demonstrate that, the proposed FL framework can achieve good performance for heart sound abnormality detection by taking the personal privacy protection into account. Moreover, using the federated feature space is beneficial to balance the interpretability of the vertical FL and the privacy of the data. This work realises the potential of FL from research to clinical practice, and is expected to have extensive application in the federated smart medical system.

Internet of things-based smart medical waste management system

Smart medical waste management is significant during the ongoing COVID-19 pandemic. This research presents a smart medical waste management system with real-time monitoring of medical waste data and an end-user application. The functional and technical design of a smart trolley by integrating various sensors for authenticated automatic lid opening and closing, waste level, weight, and storage time monitoring are discussed. Subsequently, the implementation of a cloud server and web application that provide automatic notifications to stakeholders are also presented. The users can monitor the entire process to take actions and approvals whenever necessary. The experiments are conducted to test the functionalities of the smart trolley and web application prototype. The results showed that the sensing system is efficient and avoids manual checking by sending necessary notifications. Healthcare institutions can adopt the proposed system for medical waste transfer to ensure process transparency, worker safety, and environmental cleanliness.

CATcAFSMs: Context‐based adaptive trust calculation for attack detection in fog computing based smart medical systems

<scp>CATcAFSMs</scp>: Context‐based adaptive trust calculation for attack detection in fog computing based smart medical systems

An electrochemical point-of-care testing device for specific diagnosis of the albinism biomarker based on paradigm shift designs

L-tyrosine is a recognized biomarker of albinism, whose endogenous level in human bodies is directly linked to melanin synthesis while no attention has been paid to its specific diagnosis. To this end, we have developed an electrochemical point-of-care testing device based on a molecularly imprinted gel prepared by a universal paradigm shift design to achieve the enhanced specific recognition of the L-tyrosine. Interestingly, this theoretically optimized molecularly imprinted gel validates the recognition pattern of L-tyrosine and optimizes the structure of the polymer itself with the aid of computational chemistry. Besides, modified extended-layer MXene and Au nanoclusters have significantly improved the sensing activity. As a result, the linear diagnostic range of this electrochemical point-of-care testing device for L-tyrosine is 0.1–100 μM in actual human fluids, which fully covers the L-tyrosine levels of healthy individuals and people with albinism. The diagnosis is completed in 90 s and then the results are transmitted by Bluetooth low energy to the smart mobile terminal. Therefore, we are convinced that this electrochemical point-of-care testing device is a promising tool in the future smart medical system.

Survey on A Smart Medical System on Drug Analysis using ML Algorithms

Online health communities continue to offer huge variety of medical information useful for medical practitioners, system administrators and patients alike. In this system we collect real time health posts from reputed websites, where patients express their views, including their experiences and side-effects on drugs used by them. We propose to perform Summarization of user posts per drug, and come out with useful conclusions for medical fraternity as well as patient community at a glance. Further, we propose to classify the users based on their ‘emotional state of mind’. Also, we shall perform knowledge discovery from user posts, whereby useful ‘patterns’ about the triad ‘drugs-symptoms-medicine’ is done by Association Rule Mining. Association rule mining is a popular and widely-known machine learning task. It is used to find out interesting relations between variables in large database. Rules generated by association have two disjoint set of items having form LHS (Left Hand Side) =&gt; RHS (Right Hand Side). The rule says that RHS is likely to occur whenever the LHS set occurs.

Review of Spider Silk Applications in Biomedical and Tissue Engineering.

This review will present the latest research related to the production and application of spider silk and silk-based materials in reconstructive and regenerative medicine and tissue engineering, with a focus on musculoskeletal tissues, and including skin regeneration and tissue repair of bone and cartilage, ligaments, muscle tissue, peripheral nerves, and artificial blood vessels. Natural spider silk synthesis is reviewed, and the further recombinant production of spider silk proteins. Research insights into possible spider silk structures, like fibers (1D), coatings (2D), and 3D constructs, including porous structures, hydrogels, and organ-on-chip designs, have been reviewed considering a design of bioactive materials for smart medical implants and drug delivery systems. Silk is one of the toughest natural materials, with high strain at failure and mechanical strength. Novel biomaterials with silk fibroin can mimic the tissue structure and promote regeneration and new tissue growth. Silk proteins are important in designing tissue-on-chip or organ-on-chip technologies and micro devices for the precise engineering of artificial tissues and organs, disease modeling, and the further selection of adequate medical treatments. Recent research indicates that silk (films, hydrogels, capsules, or liposomes coated with silk proteins) has the potential to provide controlled drug release at the target destination. However, even with clear advantages, there are still challenges that need further research, including clinical trials.

A machine learning-based hybrid recommender framework for smart medical systems.

This article presents a hybrid recommender framework for smart medical systems by introducing two methods to improve service level evaluations and doctor recommendations for patients. The first method uses big data techniques and deep learning algorithms to develop a registration review system in medical institutions. This system outperforms conventional evaluation methods, thus achieving higher accuracy. The second method implements the term frequency and inverse document frequency (TF-IDF) algorithm to construct a model based on the patient's symptom vector space, incorporating score weighting, modified cosine similarity, and K-means clustering. Then, the alternating least squares (ALS) matrix decomposition and user collaborative filtering algorithm are applied to calculate patients' predicted scores for doctors and recommend top-performing doctors. Experimental results show significant improvements in metrics called precision and recall rates compared to conventional methods, making the proposed approach a practical solution for department triage and doctor recommendation in medical appointment platforms.

Security enhancement of cyber-physical system using modified encryption AESGNRSA technique

&lt;span&gt;A cyber-physical system (CPS) is a combination of physical components with computational elements to interact with the physical world. The integration of these two systems has led to an increase in security concerns. Traditional encryption algorithms designed for general-purpose computing environments may not adequately address the distinct challenges of CPS, such as limited processing power, delay, and resource-constrained hardware. Therefore, there is a pressing need to develop an encryption algorithm that is optimized for CPS security without compromising the critical real-time aspects of these systems. This research has designed a modified encryption technique named the advanced encryption standard in galois counter mode with nonce and rivest-shamir-adleman algorithm (AESGNRSA). A smart medical system is designed to monitor the health of remotely located patients. The AESGNRSA algorithm is applied to the three servers of this system. The data of 1.5 lakh patients is fed to this system to verify the effectiveness of the AESGNRSA algorithm. The performance parameters like encryption and decryption time, encryption and decryption throughput, and encrypted file size are calculated for the AESGNRSA algorithm. The comparative analysis proved that AESGNRSA has the highest performance as compared to other algorithms and it can protect CPS against many cyber-attacks.&lt;/span&gt;

IoT-based centralized wireless patient tracking and surveillance system with a cutting-edge approach for advanced healthcare oversight and data-driven analysis

The construction of smart cities has been progressing quickly due to advancements in 5G and Internet of Things (IoT) technology. Smart transportation systems and smart medical ideas are also being explored alongside smart city initiatives. Integrating the two mentioned technologies, a smart navigational method for ambulances is being developed. When severe patients arrive via ambulance, significant time is wasted in relaying data, and the hospital needs to gain prior knowledge of the patient's condition. An efficient system called the IoT-based Wireless Patient Tracking Method (IoT-WPTM) is proposed to diagnose patients quickly by recording vital metrics such as heartbeat, temperature, and breathing rate using detectors. These variables are transmitted wirelessly to the healthcare before the ambulance is dispatched, allowing doctors to begin medical care promptly. If necessary, the patient monitors the ambulance's position to anticipate arrival and get immediate directions to the closest hospital. The user carries the gadget and is also placed in the ambulance. The gadget allows IoT devices and Android apps to enhance user interaction and streamline information delivery. IoT-WPTM can be surpassed with ease and in a more effective manner for smart medical surveillance systems.

Ventricular Ectopic beats detection based wavelet scattering network and ensemble bagged trees for smart medical systems

This paper introduces an automated method for classifying electrocardiograms (ECG) with the aim of enhancing both accuracy and speed in interpreting ECG results, thus contributing to the early detection of cardiovascular diseases (CVD). The model in this study employs a deep feature extraction technique combined with Ensemble Bagged Trees (EBT) to identify Ventricular Ectopic Beats (VEB). Data is sourced from the MIT-BIH Arrhythmia database, and model performance is assessed through ten-fold cross-validation. On the validation dataset, the proposed model achieves an accuracy of 98.24% and an F1 score of 97.66%. When independently tested on the MIT-BIH dataset, it demonstrates an accuracy of 98.31% and an F1 score of 89.22%. These outcomes highlight the model's ability to attain exceptional accuracy and F1 scores on both validation and testing datasets.

Smart Medical Systems Based IoT: Exploring the Relationship Between Essential Algorithms and Service Orientation

This study examines the most recent research publications that focus on the Internet of Things (IoT) in medical systems including health monitoring, fitness programs, etc., as a consequence of the IoT's explosive growth in these areas. The monitoring efficiency of IoT-based healthcare systems has been the subject of much study. In this paper, we go into the architecture of the Internet of Things, namely the cloud-integrated systems. As accuracy and power consumption are major issues in the Internet of Things, studies that aim to enhance the efficiency of healthcare systems that rely on the IoT are highlighted. This research also provides a thorough examination of the strategies used for data management in IoT-based healthcare systems that make use of cloud services. The benefits and drawbacks of the IoT-based healthcare system are discussed, as well as its performance. Studies often have a high percentage of success in identifying multiple symptoms and making illness predictions. When it comes to keeping tabs on the health of the elderly population, the Internet of Things-based healthcare system developed specifically for them is an effective answer. Existing systems have many drawbacks, including high power consumption, limited resources, and security concerns brought on by the use of several devices which are discussed in the paper.

Heart failure patients monitoring using IoT-based remote monitoring system

Intelligent health monitoring systems are becoming more important and popular as technology advances. Nowadays, online services are replacing physical infrastructure in several domains including medical services as well. The COVID-19 pandemic has also changed the way medical services are delivered. Intelligent appliances, smart homes, and smart medical systems are some of the emerging concepts. The Internet of Things (IoT) has changed the way communication occurs alongside data collection sources aided by smart sensors. It also has deployed artificial intelligence (AI) methods for better decision-making provided by efficient data collection, storage, retrieval, and data management. This research employs health monitoring systems for heart patients using IoT and AI-based solutions. Activities of heart patients are monitored and reported using the IoT system. For heart disease prediction, an ensemble model ET-CNN is presented which provides an accuracy score of 0.9524. The investigative data related to this system is very encouraging in real-time reporting and classifying heart patients with great accuracy.

Revealing the Trends in the Academic Landscape of the Health Care System Using Contextual Topic Modeling

ABSTRACT The health care system encompasses the participation of individuals, groups, agencies, and resources that offer services to address the requirements of the person, community, and population in terms of health. Parallel to the rising debates on the healthcare systems in relation to diseases, treatments, interventions, medication, and clinical practice guidelines, the world is currently discussing the healthcare industry, technology perspectives, and healthcare costs. To gain a comprehensive understanding of the healthcare systems research paradigm, we offered a novel contextual topic modeling approach that links up the CombinedTM model with our healthcare Bert to discover the contextual topics in the domain of healthcare. This research work discovered 60 contextual topics among them fifteen topics are the hottest which include smart medical monitoring systems, causes, and effects of stress and anxiety, and healthcare cost estimation and twelve topics are the coldest. Moreover, thirty-three topics are showing in-significant trends. We further investigated various clusters and correlations among the topics exploring inter-topic distance maps which add depth to the understanding of the research structure of this scientific domain. The current study enhances the prior topic modeling methodologies that examine the healthcare literature from a particular disciplinary perspective. It further extends the existing topic modeling approaches that do not incorporate contextual information in the topic discovery process adding contextual information by creating sentence embedding vectors through transformers-based models. We also utilized corpus tuning, the mean pooling technique, and the hugging face tool. Our method gives a higher coherence score as compared to the state-of-the-art models (LSA, LDA, and Ber Topic).

An Intelligent Air Monitoring System For Pollution Prediction: A Predictive Healthcare Perspective

Abstract The extensive potential of Internet of Things (IoT) technology has enabled the widespread real-time perception and analysis of health conditions. Furthermore, the integration of IoT in the healthcare industry has resulted in the development of intelligent applications, including smartphone-based healthcare, wellness-aware recommendations and smart medical systems. Building upon these technological advancements, this research puts forth an enhanced framework designed for the real-time monitoring, detection and prediction of health vulnerabilities arising from air pollution. Specifically, a four-layered model is presented to categorize health-impacting particles associated with air pollution into distinct classes based on probabilistic parameters of Health Adversity (HA). Subsequently, the HA parameters are extracted and temporally analyzed using FogBus, a fog computing platform, to identify vulnerabilities in individual health. To facilitate accurate prediction, an assessment of the Air Impact on Health is conducted using a Differential Evolution-Recurrent Neural Network. Moreover, the temporal analysis of health vulnerability employs the Self-Organized Mapping technique for visualization. The proposed model’s validity is evaluated using a challenging dataset comprising nearly 60 212 data instances obtained from the online University of California, Irvine repository. Performance enhancement is assessed by comparing the proposed model with state-of-the-art decision-making techniques, considering statistical parameters such as temporal effectiveness, coefficient of determination, accuracy, specificity, sensitivity, reliability and stability.

IRS-Driven Cybersecurity of Healthcare Cyber Physical Systems

Intelligent reflective surface (IRS) as one of the most potential 6G key technologies injects new vitality into the smart medical and health system by intelligently configuring the wireless communication environment and spawns the construction of a modern medical information system. Medical data security is a sensitive issue that hospitals are generally concerned about in recent years. There are great hidden dangers to information security in the environment of “cloud storage”. The IRS provides a new solution for enhancing the privacy and security of “cloud storage” medical file data, assisting in the efficient transmission and interconnection of information, and ensuring highly secure and uninterrupted services in healthcare-based cyber physical systems (CPS). Specifically, the modelling in this paper considers the situation where communication over multiple-input single-output (MISO) channel is eavesdropped by a patient spy holding a single antenna receiver, and additionally the patient spy is obscured by an obstacle. One of the multi-antenna base stations (BS) transmits independent data streams to doctors and keeps them secret from patient spies. By jointly optimizing the beamforming at the BS and the phase shift of the IRS reflection elements, the simulation analysis verifies that the introduction of IRS can effectively improve the privacy security of medical data.

An All‐Stretchable, Ultraviolet Protective, and Electromagnetic‐Interference‐Free E‐Textile

AbstractFlexible and skin‐mountable electronics have drawn tremendous research attention with the booming of smart medical systems and wearing technologies, however, their environmental adaptability to electromagnetic and solar radiation has long been neglected. Herein, a novel health monitoring e‐textile with robust ultraviolet (UV) protecting and strong electromagnetic interference (EMI) shielding performance is rationally developed on an ultraelastic and bilayered nonwoven textile. Via the respective incorporation of silver flake‐modified liquid metal (AgLM) and silver nanoparticles (AgNPs) on each side of a permeable substrate, a Janus sensing layer with electrophysiological monitoring function, Joule heating ability, and excellent EMI shielding capability (up to 38.5 dB in X band) is first fabricated. Elastic microfibers embedded with sensitive photochromic microcapsules are then in situ assembled on the bioelectric‐sensing layer, achieving a bilayered e‐textile with a reversible UV‐chromic property and an extraordinary UV protection factor (UPF) of 335.56. The developed all‐stretchable and UV‐EMI proof e‐textile is utilized as a safe and comfortable on‐skin electronic to provide point‐of‐care health regulation under complex UV/EMI radiative environments. Specifically, stable Joule heating performance and accurate monitoring of electrocardiogram (ECG) and surface electromyography (sEMG) are simultaneously obtained, demonstrating promising applications in multifunctional and robust wearing electronics.

Integrating Artificial Intelligence and Big Data into Smart Healthcare Systems: A Comprehensive Review of Current Practices and Future Directions

The COVID-19 pandemic has unveiled both the vulnerabilities and resilience of global healthcare systems, sparking a surge in innovation, including the accelerated adoption of Artificial Intelligence (AI) and big data analytics. This paper provides a comprehensive examination of the incorporation of these technologies within the advancement of contemporary, intelligent healthcare systems, with a keen focus on their potential to transform health management globally. This study explores the roles of AI and big data in bolstering the adaptability, efficiency, and productivity of healthcare services while also empowering individuals with actionable insights for enhanced health outcomes. Besides identifying key opportunities for AI and big data synergies within smart medical systems - encompassing disease detection and prevention, personalized medicine, resource allocation, and healthcare accessibility - this study delve into the critical ethical considerations accompanying their use. These include the essential principles of explainability, trustworthiness, privacy, security, and healthcare equity. Emphasis is placed on the importance of transparent, accountable, and ethically robust implementation strategies to ensure the responsible deployment of these technologies. Furthermore, the inherent privacy and security challenges associated with big data and AI in healthcare are addressed, detailing potential risks such as data breaches, data misuse, and patient confidentiality threats. Also, this study highlights the significance of incorporating privacy-preserving techniques, such as differential privacy and federated learning, in AI and big data analytics. This paper proposes a forward-looking paradigm for embedding AI and big data analytics within the core infrastructure of smart healthcare systems, outlining best practices and providing recommendations to leverage their transformative potential effectively and ethically. The insights and findings offered aim to guide future research, policy, and implementation efforts focused on harnessing the power of AI and big data to enhance global health resilience, stimulate innovation, and drive constructive change within the realm of intelligent medical facilities.

Wireless Sensors in General Anesthesia- An Update

Wireless sensor networks (WSNs) give wireless connectivity with computing power to monitor patients, during anesthesia in surgery. A Boyle's Continuous anesthesia machine administers anesthetic gases in combination with oxygen and real-time monitoring of oxygen saturation in blood, EEG, Body temperature, Blood pressure, heart rate, and evolved carbon dioxide (Depth of anesthesia Do A) to help anesthesiologists plan safe anesthesia. Medical sensors are attached to the patient's body to collect the patient's physiological data. Wireless transmission of the collected data to a medical staff's portable device. This update gives insight into recent developments in Wireless Networking Sensors in Critical Surgery including Anesthesia, WSN Architecture, Challenges, Network Security (Encryption) developments in Smart Medical Systems with a general domain of remote patient monitoring, focused on anesthesiology care in particular how WSN and IOT will help Anesthesia in automated administration and monitoring

Athlete injury detection and emergency treatment in mobile smart medical system

Using the sports injury monitoring system to detect injury symptoms in time and take effective treatment measures in time can reduce the damage caused by sports injuries to athletes. However, many current detection methods lack the support of advanced technologies and algorithms, resulting in poor performance in sports injury detection. Based on this, a mobile intelligent medical system is designed in this paper, and an athlete injury detection method based on CNN and sensors is proposed. The method includes three parts: motion region acquisition, motion injury feature extraction, and motion injury detection. In addition, for emergency treatment, this paper proposes a variety of CNN-based image data analysis methods to ensure the accuracy of the processing process. The experimental results show that the athlete injury detection method based on the convolutional neural network improves the detection accuracy by 6.73% compared with the traditional method, which also provides an important reference for the future application of ML in medical treatment. The research confirms that the construction and analysis of mobile intelligent medical system can effectively improve the accuracy of sports injury detection.

An Intelligent Whole-Process Medical System Based on Cloud Platform

ABSTRACT In response to the shortage of high-quality medical resources, long waiting times, and difficulties finding the right doctor, a smart medical system design scheme is proposed. This system is built on a cloud platform to increase capacity and improve operating speed. It offers intelligent triage, disease diagnosis, intelligent question-and-answer, online doctor diagnosis, online registration, patient-patient communication, doctor-patient communication, online payment, map navigation, and other functions. By using machine learning and deep learning methods, the system analyzes the patient’s disease description, laboratory test sheets, and other medical image materials to diagnose the disease suffered by the patient. The matching degree of the doctor and the patient is calculated based on the doctor’s expertise, patient evaluation, and doctor satisfaction, and doctors are arranged for patients to choose. The system also integrates the diagnosis results made by multiple doctors to help patients understand their illnesses conveniently. Additionally, an intelligent question-and-answer module analyzes the patient’s inquiry intention and gives feedback to the patient’s question. The system’s feasibility and practicability have been demonstrated by experts in the fields of medical treatment, computer, and economic management. Overall, this system has high application value and can effectively solve the problems that exist in the current medical field.